2D gait recognition methods can be classified into model-based and model-free approaches [1]}.
Early methods mainly belong to the model-based which defines a structural human body model.
Then, gait patterns are modeled by parameters like lengths of limbs, angles of joints, and relative positions of body parts [2]}, [3]}.
The model-free methods mainly adopt the silhouettes obtained by background subtraction from video frames [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}.
In particular, Han et al. proposed to aggregate a sequence of silhouettes into a compact Gait Energy Image (GEI) [4]} which was widely used by the following methods [5]}, [6]}.
Recently, due to the success of deep learning for computer vision tasks [17]}, [18]}, [19]}, [20]}, [21]}, [22]}, [23]}, [24]}, [25]}, [26]}, deep Convolutional Neural Networks (CNNs) also dominated the performance of gait recognition.
For example, Shiraga et al. [5]} and Wu et al. [6]} proposed to learn effective features from GEIs and significantly outperformed previous methods.
The most recent methods started to learn discriminative features directly from the silhouette sequences using larger CNNs or multi-scale structures and achieved state-of-the-art results [7]}, [9]}, [13]}, [12]}.
Despite the excellent performance on in-the-lab datasets, e.g., CASIA-B and OU-LP, these methods usually fail in the wild as shown in the experiments on GREW [33]} and our Gait3D.
